# Copyright 2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
import numpy as np
import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common.api import jit
from mindspore.ops.composite import GradOperation
import mindspore as ms
import torch as t
from torch.autograd import Variable

context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")


class Grad(nn.Cell):
    def __init__(self, network):
        super(Grad, self).__init__()
        self.grad = GradOperation(get_all=True, sens_param=True)
        self.network = network

    @jit
    def construct(self, input_, output_grad):
        return self.grad(self.network)(input_, output_grad)


def test_grad_3():
    """
    Feature: test bn_infer grad
    Description: test bn_infer grad with input tensor's type float32 and num_features=3
    Expectation: none.
    """
    sens = np.random.randn(1, 3, 2, 2).astype(np.float32)
    x = np.random.randn(1, 3, 2, 2).astype(np.float32)
    bn = nn.BatchNorm2d(num_features=3, use_batch_statistics=False)
    net = Grad(bn)
    input_dyn = Tensor(shape=[None, 3, None, None], dtype=ms.float32)
    sens_dyn = Tensor(shape=[None, 3, None, None], dtype=ms.float32)
    net.set_inputs(input_dyn, sens_dyn)
    ms_output = net(Tensor(x), Tensor(sens))

    torchnet = t.nn.BatchNorm2d(3, affine=True, track_running_stats=True)
    torchnet.eval()
    input_torch = Variable(t.tensor(x), requires_grad=True)
    outtorch = torchnet(input_torch)
    outtorch.backward(t.tensor(sens))

    assert np.allclose(ms_output[0].asnumpy(), input_torch.grad.numpy(), 0.0001, 0.0001)


def test_grad_64():
    """
    Feature: test bn_infer grad
    Description: test bn_infer grad with input tensor's type float32 and num_features=64
    Expectation: none.
    """
    sens = np.random.randn(1, 64, 112, 112).astype(np.float32)
    x = np.random.randn(1, 64, 112, 112).astype(np.float32)
    bn = nn.BatchNorm2d(num_features=64, use_batch_statistics=False)
    net = Grad(bn)
    input_dyn = Tensor(shape=[None, 64, None, None], dtype=ms.float32)
    sens_dyn = Tensor(shape=[None, 64, None, None], dtype=ms.float32)
    net.set_inputs(input_dyn, sens_dyn)
    ms_output = net(Tensor(x), Tensor(sens))

    torchnet = t.nn.BatchNorm2d(64, affine=True, track_running_stats=True)
    torchnet.eval()
    input_torch = Variable(t.tensor(x), requires_grad=True)
    outtorch = torchnet(input_torch)
    outtorch.backward(t.tensor(sens))

    assert np.allclose(ms_output[0].asnumpy(), input_torch.grad.numpy(), 0.0001, 0.0001)
